Summary of Alpine: Unveiling the Planning Capability Of Autoregressive Learning in Language Models, by Siwei Wang et al.
ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models
by Siwei Wang, Yifei Shen, Shi Feng, Haoran Sun, Shang-Hua Teng, Wei Chen
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the emergence of planning capabilities in Transformer-based Large Language Models (LLMs) through their next-word prediction mechanisms. The authors model planning as a network path-finding task, where they demonstrate that Transformer architectures can execute path-finding by embedding adjacency and reachability matrices within their weights. Experimental results validate these theoretical insights, showing that LLMs learn both the adjacency and a limited form of reachability matrices. The paper also highlights a fundamental limitation of current Transformer architectures in path-finding, which cannot identify reachability relationships through transitivity. This leads to failures in generating paths when concatenation is required. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how large language models can plan and make decisions. It’s like trying to find the best route from one place to another. The authors show that these models can use a special kind of math problem-solving technique to find the right path. They tested this idea and found that it works, but they also discovered a limitation in how the models work. This means that they can’t always make the best decision when things get complicated. This research helps us understand how language models think and what they’re capable of. |
Keywords
» Artificial intelligence » Embedding » Transformer
